• Develop and maintain data pipelines and transformations across the stack. Starting from ingesting transactional data into the data lakehouse to refining data up the medallion data architecture. • Tune performance, storage layout, and cost-efficiency across our data storage and query engines. • Help design and implement new data ingestion patterns and improve platform observability and reliability. • Partner with engineering, product, and operations teams to deliver well-structured, trustworthy data for diverse use cases. • Help establish and evolve best practices for our data infrastructure, from pipeline design to OpenTofu-managed resource provisioning. • Help design and implement a data governance strategy to secure our data lakehouse.

• Diseñar e implementar soluciones de datos a la medida, desde streaming hasta data lakes y analytics dentro de un stack tecnológico dinámico. • Proveer liderazgo técnico recomendando tecnologías y arquitecturas óptimas según el caso de uso. • Desarrollar soluciones en producción utilizando Python, Java o Scala, priorizando rendimiento, seguridad y escalabilidad. • Colaborar en entornos híbridos y diversos como AWS, Cloudera, Databricks y Snowflake. • Crear documentación técnica detallada (diagramas de secuencia, vistas lógicas, jerarquías de clases, etc.). • Presentar soluciones técnicas complejas a audiencias técnicas y no técnicas. • Trabajar bajo metodologías Agile. • Diseñar, construir e implementar bases de datos y data stores alineados a necesidades del negocio. • Liderar equipos técnicos en proyectos de datos end-to-end.

• Build and maintain ELT pipelines that ingest and transform data from operational systems • Develop analytics-ready data models to support reporting, KPIs, and downstream ML use cases • Collaborate with analysts and ML engineers to ensure data structures meet modeling and feature needs • Implement data quality checks, validation, and testing to ensure accuracy and reliability • Optimize data pipeline performance, scalability, and cost efficiency • Support data preparation and feature engineering workflows for machine learning initiatives • Document data models, transformations, and lineage to support reuse and data literacy • Partner with security and platform teams to ensure data governance and privacy requirements are met

• Design and maintain ETL pipelines that ingest, process, and load data into AWS Neptune • Develop and evolve graph data models representing relationships across users, sessions, devices, and security events • Integrate diverse data sources including S3, relational databases, streaming services, and APIs into a cohesive graph architecture • Build tooling and automation for data validation, transformation, enrichment, and quality monitoring • Optimize graph query performance and ensure pipeline scalability and reliability • Collaborate with DevOps and security teams to maintain compliance with FedRAMP, NIST, and data privacy requirements • Document data workflows, schemas, and best practices to support team knowledge sharing